Emerging Genetic Insight into ATIN
Authors: Atlas Khan, Ali G. Gharavi
Journal: Journal of the American Society of Nephrology (2023)

Acute tubulointerstitial nephritis (ATIN) accounts for approximately 15%–20% of acute kidney injury.1 It is often immune-mediated with prominent participation of cellular immunity, mainly T lymphocytes. Drug hypersensitivity, autoimmune disease,2 infections, or systemic disease3 can cause ATIN, or it can be idiopathic. Although members of families with ATIN share histological features and clinical symptoms,4 designing a large-scale genetic study has been challenging because diagnosis requires kidney biopsy. To date, only candidate gene studies of ATIN have been performed. However, nowadays, genome-wide association studies (GWASs), which test hundreds of thousands of genetic variants across the genome to identify variants associated with a trait of interest, are the standard strategy for unbiased examination of pathogenesis. In this issue, Zhou et al.5 performed the first GWAS for ATIN, testing single-nucleotide polymorphisms (SNPs) for associations with the disease in 544 kidney biopsy-defined cases and 2346 controls of Chinese Han ancestry6 recruited from several centers in China (Figure 1). Overall, 59% of the cases were female, and the mean age of patients was 49 years (range 12–80 years). All the controls were blood donors and therefore healthy, and this study excluded individuals with diagnoses of kidney disease as controls. The investigators discovered two genome-wide significant novel loci in the HLA and Pleckstrin Homology Domain Containing A5 (PLEKHA5) regions and performed downstream analysis. Adjusting for age, sex, and ancestry, the two loci on HLA and PLEKHA5 remained genome-wide significant (P value <5E-08). The findings did not reveal additional independent genome-wide significant signals.Figure 1: Overview of genome-wide association studies (GWAS) analysis. In GWAS analysis, data can be collected from study cohorts, microarray, or low-pass whole-genome sequencing techniques used to generate genome-wide genetic data. The quality control of GWAS includes steps, such as exclusion of low-quality SNPs and detection of population strata in the sample using PCs (A). Summary statistics were calculated for each genetic variant using an appropriate model, such as logistics or linear regression, and corrected for confounders, such as population strata (B); the red line in the Manhattan plot shows the significance threshold (P value <5E-08). In the last step of the GWAS analysis, fine mapping includes functional annotations of significant loci, pathways, or genetic correlation analyses to discover molecular drivers for inherited disease (C). PC, principal component.The functional annotation of top SNPs shows that the HLA locus rs35087390 (c.664G>A, p.Ala222Thr) is a missense variant in exon four of HLA-DQA1. Computational methods, such as Combined Annotation Dependent Depletion,7 predict the variant to be damaging. The authors also performed HLA imputation, yielding 80 two-digit classical alleles, 120 four-digit classical alleles, and 540 amino acids in HLA proteins for association analysis. Three significant signals within HLA-DQB1, HLA-DRB1, and HLA-DQA1 were observed with ATIN. Assessing polymorphic acid sites, the researchers discovered that HLA-DRB1 and HLA-DQB1 have genome-wide significant amino acid residues, with HLA-DRB1 residue 57-Ala and 60-His showing the strongest association. Interestingly, these amino acid residues were previously reported to have a significant association with kidney disease.8 Little is known about PLEKHA5, which is highly expressed in kidney, heart, and other organs. Previous studies have revealed the association of this locus with anthropomorphic traits.9 Functional annotations indicated that the top SNP rs2417771-A is a regulatory variant with significant associations with plasma vascular endothelial growth factor–A and CX3CL1 levels and did not reveal any additional insight. On the basis of functional annotations of the PLEKHA5 locus,10 the authors suggest that PLEKHA5 might interact with HLA risk in shaping the origin or progression of autoimmunity. Perhaps this hypothesis could be tested in a larger cohort of patients with ATIN. Protein-protein interaction pathway analysis (Search Tool for the Retrieval of Interacting Genes/Proteins) identified antigen processing and presentation, asthma, allograft rejection, and Th1 and Th2 cell differentiation as the top Kyoto Encyclopedia of Genes and Genomes pathways, probably primarily driven by the HLA signal. In addition, the authors estimated SNP-based heritability for ATIN with and without the HLA locus as 20.43% and 10.35%,11 respectively. The only HLA lead variant, rs35087390, accounts for 3.90% of the variance of ATIN. Finally, the authors performed subphenotype and clinical correlation analyses for drug-induced ATIN and autoimmunity-associated ATIN but did not observe additional genome-wide signals specific to these phenotypes. They observed that patients with risk genotypes have higher creatinine at both biopsy and peak and higher urinary biomarker levels, including proteinuria, N-acetyl-β-d-glucosaminidase, and α1-microglobulin. Interestingly, patients carrying the risk genotypes (AA) were more likely to be on immunosuppressive therapy and showed higher renal function recovery (87.50%). These findings require replication but suggest that genetic data may predict response to therapy. This study has several important limitations. First, data were pooled across heterogeneous patients with diverse forms of ATIN. Further investigation should examine phenotype heterogeneity. In addition, all participants were of Chinese Han ancestry. Testing more ancestrally diverse cohorts is required to demonstrate the relevance of these findings for global populations. In summary, the well-performed GWAS by Zhou et al. is an essential step in investigating ATIN pathogenesis, suggesting that follow-up genetic studies can provide additional insight. The HLA association places ATIN in the spectrum of autoimmune/inflammatory diseases, whereas the PLEKHA5 signal suggests a novel pathway that requires further scrutiny. Future studies should increase the sample size to detect additional novel signals, examine subtypes of ATIN separately, and extend the analysis to more diverse populations. Moreover, GWAS only evaluates common genetic variants with minor allele frequency (MAF) >1%, whereas rare variants can also play an essential role in human disease.12 Therefore, future studies will need to assess rare variants (MAF <1%), which could explain the additional risk of illness. Finally, researchers have recently developed genome-wide polygenic risk scores (PRSs), which aggregate the cumulative effects of millions of SNPs. They are useful to screen complex conditions in early stages, such as CKD.13 A PRS for ATIN may enable genetic risk stratification, which could affect the use of antibiotics that might be nephrotoxic. A PRS may eventually also guide noninvasive diagnosis of ATIN when kidney biopsy poses a risk. An accurate PRS requires a much larger sample, but the study by Zhou et al. is perhaps a first step in developing the large cohorts necessary to gain insight into ATIN pathogenesis through genetic approaches.